Mapping of the Greenhouse Gas Emission Potential for the Offshore Wind Power Sector in Guangdong, China

Zetao Huang, Youkai Yu, Yushu Chen, Tao Tan and Xuhui Kong ()
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Zetao Huang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Youkai Yu: Institute for Innovation and Entrepreneurship, Loughborough University, London E20 3BS, UK
Yushu Chen: Institute of Biomass Engineering, South China Agricultural University, Guangzhou 510642, China
Tao Tan: Institute of Biomass Engineering, South China Agricultural University, Guangzhou 510642, China
Xuhui Kong: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China

Sustainability, 2022, vol. 14, issue 23, 1-14

Abstract: This study aims to assess the potential greenhouse gas (GHG) emissions of delivering 1 kWh from planned offshore wind farm sites to the grid in the Guangdong Province, China. In contrast to most previous studies, we avoided underestimating GHG emissions per kWh by approximately 49% by adopting a spatialized life-cycle inventory (LCI)-improved stock-driven model under the medium scenario combination. We also developed a callable spatialized LCI to model the spatial differences in the GHG emissions per kWh by cells in planned offshore wind farm sites in Guangdong. The modeling results indicate that, under the medium scenario combination, the GHG emissions per kWh will range from 4.6 to 19 gCO 2eq /kWh and the cells with higher emissions are concentrated in the deep-water wind farms in the eastern ocean of the Guangdong Province. According to the mechanism by which the different scenarios affect the modeling results, increasing the unit capacity of turbines is the most effective approach for reducing the GHG emissions per kWh and decreasing the impact of natural conditions. Air density can be used as an empirical spatial variable to predict the GHG emission potential of planned wind farm sites in Guangdong. The modeling framework in this study provides a more reliable quantitative tool for decision-makers in the offshore wind sector that can be used directly for any offshore wind system with a monopile foundation and be extended to wind power systems with other foundation types, or even to the entire renewable energy and other infrastructure systems after certain modifications.

Keywords: dynamic material flow analysis; life-cycle assessment; climate change; carbon neutral; spatial analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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